Konferenzen

Stefan Voß (Primary Contact)
University of Hamburg
Institute of Information Systems (IWI)
Von-Melle-Park 5, 20146 Hamburg, Germany
Tel : +49-40-42838-3062, fax: +49-40-42838-5535
Email : stefan.voss@uni-hamburg.de

Leonard Heilig
University of Hamburg
Institute of Information Systems (IWI)
Von-Melle-Park 5, 20146 Hamburg, Germany
Tel: +49-40-42838-4294, fax: +49-40-42838-5535
Email: leonard.heilig@uni-hamburg.de

This tutorial will provide insights of decision-related issues and challenges in the context of cloud

computing. The goal of this tutorial is to achieve a broad understanding of decision analytics
techniques and their link to utility-oriented IT services provided by a cloud. The participants
thereby explore relevant quantitative factors that affect decision making of consumers and
providers. We discuss different ways of considering these factors and examine a wide variety of
quantitative models and techniques for managing efficient decision support in real-world cloud
environments.

We will provide a brief introduction to decision analytics and examine the current state of
decision analytics in the context of cloud computing. In particular, we regard design principles,
methods and tools for supporting decision-making processes of cloud consumers and providers.
An integrative view on these decision problems and approaches is especially interesting for
cloud intermediaries that provide cloud services based on the cloud infrastructure of one or
several cloud providers. An example of such an intermediary is the Adobe Shared Cloud, the
platform as a service (PaaS) for multiple software as a service (SaaS) solutions (e.g., Adobe
Creative Cloud) that relies on cloud infrastructure and services of Amazon Web Services (AWS).
To gain value, the intermediary has to make decisions from a vendor’s perspective (e.g., pricing

decisions) as well as from the consumer’s perspective (e.g., selection of cloud services provided
by one or several cloud providers, redundancy allocation, etc.) in order to operate its service
according to Quality of Service (QoS) requirements and/or to fulfill service-level agreements
(SLAs). In this regard, there is a great potential for improvement which can solely be utilized by
intensively studying decision analytics in the context of cloud computing.
The material of the tutorial focuses on core decision-related issues of cloud computing currently
discussed in the literature. The tutorial gives a deeper insight in service selection and
outsourcing decisions (i.e., cloud adoption decisions) of consumers in general and demonstrates
the application of decision analytics. Regarding cloud service selection, we explain means to
efficiently match cloud services with various, prioritized functional and non-functional
requirements of consumers defined through QoS vectors. Thereby we address the modeling of
the QoS-constrained multi-criteria and multi-dimensional decision problems and show how these
problems can be solved with exact and approximate methods under local and global constraints.
On the other hand, the tutorial reviews decision-related issues of cloud providers, such as pricing
and scheduling decisions. In this context, we examine QoS constraint scheduling decisions and
discuss different means to support management decisions of cloud providers regarding their
cloud infrastructure. This involves energy-aware decision analytics approaches. In addition, the
tutorial reviews tools (e.g., CloudSim) to simulate cloud environments as a vital basis for the
evaluation of experiments. Finally, we aim to discuss the interdependencies between consumerand
provider-related decisions and how to manage the potential trade-off.
The tutorial will have the following structure:

1. Introduction into Cloud Computing and Decision Analytics
2. Scientometric Analysis: State of the Art and Future Research Trends
3. Decision Analytics for Cloud Service Selection (consumer’s perspective)
4. Decision Analytics for Cloud Outsourcing Decisions (consumer’s perspective)
5. Decision Analytics for Cloud Pricing (vendor’s perspective)
6. Summary and Conclusions

Stefan Voß is professor and director of the Institute of Information Systems at the University of
Hamburg. Previous positions include (full) professor and head of the department of Business
Administration, Information Systems and Information Management at the University of
Technology Braunschweig (Germany) from 1995 up to 2002. He holds degrees in Mathematics
(diploma) and Economics from the University of Hamburg and a Ph.D. and the habilitation from
the University of Technology Darmstadt. His current research interests are in quantitative /
information systems approaches to supply chain management and logistics including
applications in maritime shipping, public mass transit and telecommunications. He is author and
co-author of several books and numerous papers in various journals. Stefan Voß serves on the
editorial board of some journals including being Editor of Netnomics and Editor of Public
Transport. He is frequently organizing workshops and conferences. Furthermore, he is consulting
with several companies.

Leonard Heilig holds a B.Sc. (Münster, Germany) and a M.Sc. (Hamburg, Germany) in
Information Systems. Currently he holds a position at the Institute of Information Systems at the
University of Hamburg. He spent some time at the University of St Andrews (Scotland, UK)
focusing on security management, web technologies and software engineering. Practical
experiences include work at companies like Adobe Systems, EADS Innovation Works and
Beiersdorf Shared Services. His current interest focuses on cloud computing. Related
applications incorporate mobile workforce management systems.

Intelligent decision support is known to be crucial in many business contexts, and it will both gain momentum and become increasingly challenging through the availability of very large data pools (big data). Sensor networks, social network activities, RFID deployment, Internet search histories and retail transactions are just a few examples of sources that are likely to generate Exabytes or even Zettabytes of data. The data are characterized by high levels of volume, velocity, variety and variability, and traditional analytics and techniques may easily fall short of storing, analyzing and processing these data and, even more so, using them in an intelligent way for decision making.

Key advantages of successfully managing big data and using them for decision making and business analytics include the improvement of overall efficiency, the improvement of speed and accuracy of decision making, the ability to forecast, the identification of business opportunities and a greater understanding of citizens’ and customers’ needs. Turning big data into business and society value will thus become one of the major challenges in the IS discipline. The close link between data and decisions shall avoid the processing of irrelevant or redundant information and thus help reduce information overload while ensuring that all relevant information is processed.

This track looks for new and innovative methodologies, techniques, theories, and systems that allow for exploiting big data pools to support decision makers. We invite both quantitative and analytical contributions.

Possible topics include, but are not limited to:

• Data mining and machine learning in decision support contexts,
• Decision making, including optimization models and methodologies
• Uncertainty and risk management in decision support
• Iterative, sequential and interdependent decisions
• Social media analysis and crowd sourcing
• Collaborative and participatory decision making including social media applications
• Retail and customer analysis
• Logistics and supply chain analysis and supply chain risk management
• Internet data analysis
• Data quality
• Business value of big data
• Video analytics
• Geospatial analytics
• Cloud analytics and intelligence

Paper Submission begins: November 1, 2013
Submission Deadline Date: December 8, 2013
Notification of Acceptance: March 3, 2014
Final version of accepted papers due: March 30, 2014
Early Bird Registration closes: April 16, 2014

• Jörn Altmann, Seoul National University, Korea
• Christer Carlsson, Abo Akademi University, Finland
• Haluk Demirkan, Arizona State University, USA
• Benjamin Fabian, Humboldt-Universität zu Berlin, Germany
• Andreas Fink, Helmut-Schmidt-University, Germany
• Ole-Christoffer Granmo, University of Agder, Norway
• Richard F. Hartl, University of Vienna, Austria
• Laetitia Jourdan, INRIA, France
• Mathias Klier, University of Regensburg, Germany
• Nathalie Kliewer, Freie Universität Berlin, Germany
• Leo Kroon, Rotterdam School of Management, The Netherlands
• Janny M.Y Leung, The Chinese University of Hong Kong, China
• Dirk C. Mattfeld, Technische Universität Braunschweig
• Lars Mönch, Fernuniversität Hagen, Germany
• Leysia Palen, University of Colorado, Boulder, USA
• Fethi A. Rabhi, University of New South Wales, Australia
• Franz Rothlauf, Johannes Gutenberg University of Mainz, Germany
• Vicente Salas Fumás, Universidad de Zaragoza, Zaragoza, Spain
• Michael Scholz, Universität Passau, Germany

Hans-Jürgen Sebastian, RWTH Aachen University, Germany      

Stefan Voß, University of Hamburg, Germany   (primary contact person)

This minitrack features theoretical developments, real-world applications and software solutions related to solving decision problems in logistics and supply chain management. Methods include optimization, heuristics and metaheuristics, simulation, agent technologies, and descriptive methods.

We seek papers dealing with decision technologies that contribute to intelligent decision support in the whole field of logistics and in particular in all categories of SCM. This includes but is not restricted to simulation, optimization, heuristics, metaheuristics, agent technologies, decision analytics, descriptive models, data warehousing, and data mining. We are especially interested in real-world applications and in software solutions that assist in solving decision problems. This is extended towards, e.g., computational logistics, advanced planning systems and the intelligent use of ERP systems. Also conceptual ideas, reports on projects in progress, and case studies are welcome. Moreover, teaching cases
both at the university as well as the executive level may be of interest. Information technology (IT) and information systems (IS) are prerequisites and enablers for successful supply chain management (SCM). With related advances, the logistics and SCM field is developing very dynamically. Business-tobusiness transactions are made via the Internet and enterprise resource planning (ERP) systems support managing the transactional information within the enterprise. While IT and IS are vital components in supply chains, their successful management rests on intelligent and coordinated decision making throughout the logistics network. Intelligent decision support that is using advanced decision technologies and analytics methodology is becoming increasingly important in logistics and SCM. Data warehouses and data mining can be used to store and analyze product, inventory, and sales information. Simulation and optimization, which can be found in advanced planning and scheduling systems, can be employed for, e.g., inventory, production, procurement, and distribution planning. Intelligent agents can, e.g., communicate with different partners in the supply chain, assist in collecting information, share product information,
negotiate prices, and distribute alerts throughout the logistics networks. The design and implementation of intelligent decision technologies to support human agents in logistics and SCM is a very active field in research, consulting and software development. Many such technologies or systems are continuously being developed, implemented and used in real-world scenarios. Therefore, we believe that both the scientific community and practitioners developing or using logistics and SCM solutions will recognize this minitrack.

June 15, 2013     submission deadline
August 15, 2013     REVIEW PROCESS: Notification

Stefan Voß, University of Hamburg, Germany

The minitrack considers original and fundamental work in the field of telecommunications, internet and media technoeconomics. More specifically, the minitrack features theoretical developments, real-world applications and software solutions related to Telecommunications Analytics and Economics. Methods include game theory, optimization, heuristics and meta-heuristics, simulation, agent technologies, and descriptive methods.

Telecommunications network design and related economic and business aspects are an ongoing research topic. Demand is changing continuously and telecommunications service providers have to cope with extended demands as well as quality of service (QoS) requirements. Moreover, the standardization of advanced mobile communication systems, in particular of new technologies like Long Term Evolution (LTE) and LTE Advanced imply new issues in mobile communications regarding available bandwidth. To take advantage of the full feature set of next generation networks, transport network design has to face new requirements, caused by architectural changes and technologies. Also, newly defined interfaces impact the transport network requirements. At the same time a tendency of locating network elements at fewer, central sites to reduce operational expenditure can be observed, in particular concerning the transport layer. That is, we have to look at the mobile market as well as to questions related to fiber-to-the-home developments, questions of how to analyse and design or re-design telecommunications networks and services from a technology and economics standpoint. This minitrack aims to support the research in these directions by accommodating original contributions from researchers working in all possible aspects of telecommunications techno-economics. This is done from various viewpoints like systems engineers, decision analytics specialists and economists, just to name a few, underlying the interdisciplinary and multifaceted character of telecommunications techno-economics. Topics include but are not limited to behaviour analysis, regulation, network design, policy and management, mobile services growth models, pricing of internet service providers (ISP), interplay of QoS and price, network effects and network externalities. Selected papers from this minitrack are expected to be fast-tracked for a special issue of the journal Netnomics.

 June 15, 2013    submission deadline
August 15, 2013    REVIEW PROCESS: Notification

Andreas Fink, Helmut-Schmidt-University Hamburg, Germany    

Hans-Jürgen Sebastian, RWTH Aachen University, Germany      

Stefan Voß, University of Hamburg, Germany   (primary contact person)

The minitrack features theoretical developments, real-world applications and software solutions related to solving decision problems in logistics and supply chain management. Methods include optimization, heuristics and meta-heuristics, simulation, agent technologies, and descriptive methods.

This minitrack is a continuation of the successful minitrack "Intelligent Decision Support for (e-)Logistics and Supply Chain Management" of HICSS-36 through HICSS-43. In each case accepted papers were usually being presented in two or three sessions and an open forum on future issues of e-Logistics took place. The sessions were always attended by 15 to 20 people with an exception in 2005 where attendance was somewhat higher at about 20-25. Attendance of 2010 is still open but expected to be n the same line as before.

Information technology (IT) is a prerequisite for successful supply chain management (SCM) today and will become even more so in near future. The logistics and SCM field is developing very dynamically. Business-to-business transactions are made via the Internet and enterprise resource planning (ERP) systems that manage the transactional information within the enterprise. While IT systems are vital components in supply chains, their successful management rests on intelligent and coordinated decision making throughout the logistics network. Intelligent decision support using advanced decision technologies are becoming increasingly important in logistics and SCM. Data warehouses and data mining can be used to store and analyze product, inventory, and sales information. Simulation and optimization, which can be found in advanced planning and scheduling systems, can be employed for, e.g., inventory, production, procurement, and distribution planning. Intelligent agents can e.g. communicate with different partners in the supply chain, assist in collecting information, share product information, negotiate prices, and distribute alerts throughout the logistics networks. The design and implementation of intelligent decision technologies to support human agents in logistics and SCM is a very active field in research, consulting and software development. Many such technologies or systems have been implemented recently or are currently in the stage of implementation. We do, therefore, believe that this minitrack will be recognized by both the scientific community and practitioners developing or using logistics and SCM solutions.

We aim again at organizing a minitrack consisting of two or three sessions depending on the number of high quality submissions. We seek papers dealing with decision technologies which contribute to intelligent decision support in the whole field of logistics and in particular in all categories of SCM. This includes but is not restricted to simulation, optimization, heuristics, metaheuristics, agent technologies, descriptive models, data warehousing, and data mining. We are especially interested in real-world applications and in software solutions which assist in solving decision problems. This is extended towards, e.g., advanced planning systems and the intelligent use of enterprise resource planning systems. Also conceptual ideas, reports on projects in progress, and case studies are welcome. Moreover, teaching cases both at the university as well as the executive level may be of interest.

June 15, 2010    submission deadline
August 15, 2010    REVIEW PROCESS: Notification

Stefan Voß, University of Hamburg, Germany   (primary contact person)

Andreas Fink, Helmut-Schmidt-University Hamburg, Germany   

Hans-Jürgen Sebastian, RWTH Aachen University, Germany     

Information technology (IT) is a prerequisite for successful supply chain management (SCM) today and will become even more so in near future. The logistics and SCM field is developing very dynamically. Business-to-business transactions are made via the Internet and enterprise resource planning (ERP) systems that manage the transactional information within the enterprise. While IT systems are vital components in supply chains, their successful management rests on intelligent and coordinated decision making throughout the logistics network. Intelligent decision support using advanced decision technologies are becoming increasingly important in logistics and SCM. Data warehouses and data mining can be used to store and analyze product, inventory, and sales information. Simulation and optimization, which can be found in advanced planning and scheduling systems, can be employed for, e.g., inventory, production, procurement, and distribution planning. Intelligent agents can e.g. communicate with different partners in the supply chain, assist in collecting information, share product information, negotiate prices, and distribute alerts throughout the logistics networks. The design and implementation of intelligent decision technologies to support human agents in logistics and SCM is a very active field in research, consulting and software development. Many such technologies or systems have been implemented recently or are currently in the stage of implementation. We do, therefore, believe that this minitrack will be recognized by both the scientific community and practitioners developing or using logistics and SCM solutions. We seek papers dealing with decision technologies which contribute to intelligent decision support in the whole field of logistics and in particular in all categories of SCM. This includes but is not restricted to simulation, optimization, heuristics, metaheuristics, agent technologies, descriptive models, data warehousing, and data mining. We are especially interested in real-world applications and in software solutions which assist in solving decision problems. This is extended towards, e.g., advanced planning systems and the intelligent use of enterprise resource planning systems. Also conceptual ideas, reports on projects in progress, and case studies are welcome. Moreover, teaching cases both at the university as well as the executive level may be of interest.

Authors may contact minitrack co-chairs for guidance and indication of appropriate content at anytime
June 15, 2009    Authors submit full papers
August 15, 2009     Notice of acceptance, conditional acceptance, or rejection
September 10, 2009     Revised version of paper due in case of conditional acceptance
September 15, 2009     Final version of paper due to publisher. At least one author of each paper must register by this date.